Global Health and Development (2012-2021)
Baza danych zoostała pobrana z https://www.kaggle.com/datasets/martinagalasso/global-health-and-development-2012-2021?select=global_health.csv 9.12.2024
Zaimpotrowanie Biliotek
Bilioteka pycountry_convert odróżnia które państwa należą do danego kontynentu
Zainicjowanie pliku global_health.csv i przyjrzeniu się danym
In [1]:
import pandas as pd
import pycountry_convert as pc
import matplotlib.pyplot as plt
file_path = 'global_health.csv'
data = pd.read_csv(file_path)
print(data)
Country Country_Code Year Fertility_Rate \
0 Afghanistan AFG 2012 5.830
1 Afghanistan AFG 2013 5.696
2 Afghanistan AFG 2014 5.560
3 Afghanistan AFG 2015 5.405
4 Afghanistan AFG 2016 5.262
... ... ... ... ...
1875 Zimbabwe ZWE 2017 3.706
1876 Zimbabwe ZWE 2018 3.659
1877 Zimbabwe ZWE 2019 3.599
1878 Zimbabwe ZWE 2020 3.545
1879 Zimbabwe ZWE 2021 3.491
Urban_Population_Percent Total_Population Water_Access_Percent \
0 24.160 30466479.0 21.123996
1 24.373 31541209.0 22.034470
2 24.587 32716210.0 22.944301
3 24.803 33753499.0 23.853590
4 25.020 34636207.0 24.762220
... ... ... ...
1875 32.237 14751101.0 26.944588
1876 32.209 15052184.0 26.807938
1877 32.210 15354608.0 26.683978
1878 32.242 15669666.0 26.573846
1879 32.303 15993524.0 26.476995
Unemployment_Rate Sanitary_Expense_Per_GDP Life_Expectancy ... \
0 7.909 7.897169 61.923 ...
1 7.919 8.805964 62.417 ...
2 7.915 9.528878 62.545 ...
3 9.011 10.105348 62.659 ...
4 10.100 11.818590 63.136 ...
... ... ... ... ...
1875 6.347 6.363583 60.709 ...
1876 6.792 4.670392 61.414 ...
1877 7.373 3.232802 61.292 ...
1878 8.621 2.954401 61.124 ...
1879 9.540 2.789751 59.253 ...
Sanitary_Expense_Per_Capita CO2_Exposure_Percent Air_Pollution \
0 52.613541 70.922317 70.922317
1 56.305542 73.131816 73.131816
2 60.189579 77.143728 77.143728
3 60.058540 73.490818 73.490818
4 61.486458 72.765910 72.765910
... ... ... ...
1875 92.254288 17.997754 17.997754
1876 114.632950 19.112099 19.112099
1877 54.808506 18.528607 18.528607
1878 50.676510 19.494180 19.494180
1879 62.737244 NaN NaN
Labour_Force_Total Tuberculosis_Per_100000 Suicide_Rate_Percent \
0 7520865.0 189.0 3.68
1 7881567.0 189.0 3.66
2 8285362.0 189.0 3.60
3 8630724.0 189.0 3.57
4 8913938.0 189.0 3.61
... ... ... ...
1875 5626689.0 221.0 15.02
1876 5770751.0 210.0 14.00
1877 5912685.0 199.0 14.12
1878 6001349.0 188.0 NaN
1879 6182422.0 199.0 NaN
Obesity_Rate_Percent Underweight_Rate_Percent Overweight_Rate_Percent \
0 10.70 10.15 31.55
1 11.55 10.79 32.73
2 10.44 10.17 33.95
3 11.19 10.52 35.19
4 11.99 7.88 36.45
... ... ... ...
1875 10.27 9.99 17.43
1876 10.69 5.18 17.81
1877 11.11 5.14 18.22
1878 11.53 5.11 18.65
1879 11.97 5.08 19.11
Safe_Water_Access_Percent
0 46.68
1 49.45
2 52.25
3 55.09
4 57.97
... ...
1875 50.06
1876 49.46
1877 48.87
1878 48.28
1879 47.70
[1880 rows x 29 columns]
Wypisanie wszystkich kolumn w liście
In [2]:
data.columns.tolist()
Out[2]:
['Country', 'Country_Code', 'Year', 'Fertility_Rate', 'Urban_Population_Percent', 'Total_Population', 'Water_Access_Percent', 'Unemployment_Rate', 'Sanitary_Expense_Per_GDP', 'Life_Expectancy', 'Life_Expectancy_Female', 'Life_Expectancy_Male', 'Infant_Deaths', 'GDP_Per_Capita', 'Hospital_Beds_Per_1000', 'Female_Population', 'Male_Population', 'Alcohol_Consumption_Per_Capita', 'Immunization_Rate', 'Sanitary_Expense_Per_Capita', 'CO2_Exposure_Percent', 'Air_Pollution', 'Labour_Force_Total', 'Tuberculosis_Per_100000', 'Suicide_Rate_Percent', 'Obesity_Rate_Percent', 'Underweight_Rate_Percent', 'Overweight_Rate_Percent', 'Safe_Water_Access_Percent']
Wypisanie państw by zobaczyć jakie mają nazwy
In [3]:
unique_countries = data['Country'].unique()
unique_countries_count = len(unique_countries)
unique_countries_count, unique_countries
Out[3]:
(188,
array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',
'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',
'Austria', 'Azerbaijan', 'Bahamas, The', 'Bahrain', 'Bangladesh',
'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',
'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',
'Brunei Darussalam', 'Bulgaria', 'Burkina Faso', 'Burundi',
'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada',
'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',
'Comoros', 'Congo, Dem. Rep.', 'Congo, Rep.', 'Costa Rica',
"Cote d'Ivoire", 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark',
'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador',
'Egypt, Arab Rep.', 'El Salvador', 'Equatorial Guinea', 'Eritrea',
'Estonia', 'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France',
'Gabon', 'Gambia, The', 'Georgia', 'Germany', 'Ghana', 'Greece',
'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana',
'Haiti', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia',
'Iran, Islamic Rep.', 'Iraq', 'Ireland', 'Israel', 'Italy',
'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',
'Korea, Rep.', 'Kuwait', 'Kyrgyz Republic', 'Lao PDR', 'Latvia',
'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein',
'Lithuania', 'Luxembourg', 'Madagascar', 'Malawi', 'Malaysia',
'Maldives', 'Mali', 'Malta', 'Marshall Islands', 'Mauritania',
'Mauritius', 'Mexico', 'Micronesia, Fed. Sts.', 'Moldova',
'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique',
'Myanmar', 'Namibia', 'Nauru', 'Nepal', 'Netherlands',
'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'North Macedonia',
'Norway', 'Oman', 'Pakistan', 'Palau', 'Panama',
'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',
'Portugal', 'Qatar', 'Romania', 'Russian Federation', 'Rwanda',
'Samoa', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia',
'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',
'Slovak Republic', 'Slovenia', 'Solomon Islands', 'South Africa',
'South Sudan', 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Sweden',
'Switzerland', 'Syrian Arab Republic', 'Tajikistan', 'Tanzania',
'Thailand', 'Timor-Leste', 'Togo', 'Tonga', 'Trinidad and Tobago',
'Tunisia', 'Turkiye', 'Turkmenistan', 'Tuvalu', 'Uganda',
'Ukraine', 'United Arab Emirates', 'United Kingdom',
'United States', 'Uruguay', 'Uzbekistan', 'Vanuatu',
'Venezuela, RB', 'Viet Nam', 'Yemen, Rep.', 'Zambia', 'Zimbabwe'],
dtype=object))
Przyporządkowanie kodu kontynentu do państwa
In [4]:
def get_continent(country_name):
try:
country_alpha2 = pc.country_name_to_country_alpha2(country_name)
continent_code = pc.country_alpha2_to_continent_code(country_alpha2)
return continent_code
except KeyError:
return "Unknown"
data['Continent_Code'] = data['Country'].apply(get_continent)
data[['Country', 'Continent_Code']].head()
Out[4]:
| Country | Continent_Code | |
|---|---|---|
| 0 | Afghanistan | AS |
| 1 | Afghanistan | AS |
| 2 | Afghanistan | AS |
| 3 | Afghanistan | AS |
| 4 | Afghanistan | AS |
Przyjżenie się czy nie brakuje danych
In [13]:
data.count()
Out[13]:
Country 1880 Country_Code 1880 Year 1880 Fertility_Rate 1844 Urban_Population_Percent 1880 Total_Population 1880 Water_Access_Percent 1225 Unemployment_Rate 1740 Sanitary_Expense_Per_GDP 1834 Life_Expectancy 1840 Life_Expectancy_Female 1840 Life_Expectancy_Male 1840 Infant_Deaths 1870 GDP_Per_Capita 1853 Hospital_Beds_Per_1000 1281 Female_Population 1880 Male_Population 1880 Alcohol_Consumption_Per_Capita 1624 Immunization_Rate 1870 Sanitary_Expense_Per_Capita 1833 CO2_Exposure_Percent 1683 Air_Pollution 1683 Labour_Force_Total 1740 Tuberculosis_Per_100000 1670 Suicide_Rate_Percent 1272 Obesity_Rate_Percent 1650 Underweight_Rate_Percent 1650 Overweight_Rate_Percent 1650 Safe_Water_Access_Percent 1648 Continent_Code 1880 dtype: int64
Z sumowanie ile jest pustych wartości w kolumnach
In [15]:
data.isnull().sum()
Out[15]:
Country 0 Country_Code 0 Year 0 Fertility_Rate 36 Urban_Population_Percent 0 Total_Population 0 Water_Access_Percent 655 Unemployment_Rate 140 Sanitary_Expense_Per_GDP 46 Life_Expectancy 40 Life_Expectancy_Female 40 Life_Expectancy_Male 40 Infant_Deaths 10 GDP_Per_Capita 27 Hospital_Beds_Per_1000 599 Female_Population 0 Male_Population 0 Alcohol_Consumption_Per_Capita 256 Immunization_Rate 10 Sanitary_Expense_Per_Capita 47 CO2_Exposure_Percent 197 Air_Pollution 197 Labour_Force_Total 140 Tuberculosis_Per_100000 210 Suicide_Rate_Percent 608 Obesity_Rate_Percent 230 Underweight_Rate_Percent 230 Overweight_Rate_Percent 230 Safe_Water_Access_Percent 232 Continent_Code 0 dtype: int64
Lista brakujących danych w kolumnie "Water_Access_Percent"
In [17]:
data[data['Water_Access_Percent'].isnull()]
Out[17]:
| Country | Country_Code | Year | Fertility_Rate | Urban_Population_Percent | Total_Population | Water_Access_Percent | Unemployment_Rate | Sanitary_Expense_Per_GDP | Life_Expectancy | ... | CO2_Exposure_Percent | Air_Pollution | Labour_Force_Total | Tuberculosis_Per_100000 | Suicide_Rate_Percent | Obesity_Rate_Percent | Underweight_Rate_Percent | Overweight_Rate_Percent | Safe_Water_Access_Percent | Continent_Code | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | Angola | AGO | 2012 | 6.039 | 61.268 | 25188292.0 | NaN | 16.557 | 2.395750 | 58.623 | ... | 24.480493 | 24.480493 | 10527326.0 | 380.0 | 11.23 | 11.36 | 10.12 | 18.73 | 25.61 | AF |
| 41 | Angola | AGO | 2013 | 5.953 | 62.002 | 26147002.0 | NaN | 16.487 | 2.732827 | 59.307 | ... | 24.538658 | 24.538658 | 10886364.0 | 376.0 | 11.31 | 11.70 | 9.97 | 19.04 | 25.98 | AF |
| 42 | Angola | AGO | 2014 | 5.864 | 62.731 | 27128337.0 | NaN | 16.401 | 2.434129 | 60.040 | ... | 23.945584 | 23.945584 | 11251540.0 | 370.0 | 10.44 | 12.06 | 11.00 | 19.36 | 26.35 | AF |
| 43 | Angola | AGO | 2015 | 5.774 | 63.446 | 28127721.0 | NaN | 16.491 | 2.605795 | 60.655 | ... | 25.002150 | 25.002150 | 11623549.0 | 366.0 | 10.46 | 12.44 | 10.82 | 19.67 | 26.71 | AF |
| 44 | Angola | AGO | 2016 | 5.686 | 64.149 | 29154746.0 | NaN | 16.580 | 2.713150 | 61.092 | ... | 25.180701 | 25.180701 | 12011479.0 | 362.0 | 9.96 | 12.82 | 10.65 | 20.01 | 27.08 | AF |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1865 | Zambia | ZMB | 2017 | 4.614 | 42.976 | 17298054.0 | NaN | 8.518 | 4.781562 | 62.120 | ... | 23.758282 | 23.758282 | 5594492.0 | 361.0 | 13.63 | 11.12 | 10.54 | 16.66 | 47.73 | AF |
| 1866 | Zambia | ZMB | 2018 | 4.536 | 43.521 | 17835893.0 | NaN | 5.031 | 4.769967 | 62.342 | ... | 23.336720 | 23.336720 | 5789287.0 | 346.0 | 13.23 | 11.74 | 10.19 | 17.33 | 48.86 | AF |
| 1867 | Zambia | ZMB | 2019 | 4.451 | 44.072 | 18380477.0 | NaN | 5.539 | 6.753325 | 62.793 | ... | 23.203748 | 23.203748 | 6117559.0 | 333.0 | 12.01 | 12.38 | 8.07 | 18.03 | 49.99 | AF |
| 1868 | Zambia | ZMB | 2020 | 4.379 | 44.629 | 18927715.0 | NaN | 6.032 | 6.316198 | 62.380 | ... | 24.308592 | 24.308592 | 6488754.0 | 319.0 | NaN | 13.04 | 7.93 | 18.77 | 51.12 | AF |
| 1869 | Zambia | ZMB | 2021 | 4.308 | 45.192 | 19473125.0 | NaN | 5.195 | 6.624165 | 61.223 | ... | NaN | NaN | 6642413.0 | 307.0 | NaN | 13.72 | 7.80 | 19.54 | 51.12 | AF |
655 rows × 30 columns
Sprawdzenie ile zostanie danych po wykluczeniu wierszy z pustymi wartościami
In [19]:
data_noNA=data.dropna().copy()
In [21]:
data_noNA.count()
Out[21]:
Country 669 Country_Code 669 Year 669 Fertility_Rate 669 Urban_Population_Percent 669 Total_Population 669 Water_Access_Percent 669 Unemployment_Rate 669 Sanitary_Expense_Per_GDP 669 Life_Expectancy 669 Life_Expectancy_Female 669 Life_Expectancy_Male 669 Infant_Deaths 669 GDP_Per_Capita 669 Hospital_Beds_Per_1000 669 Female_Population 669 Male_Population 669 Alcohol_Consumption_Per_Capita 669 Immunization_Rate 669 Sanitary_Expense_Per_Capita 669 CO2_Exposure_Percent 669 Air_Pollution 669 Labour_Force_Total 669 Tuberculosis_Per_100000 669 Suicide_Rate_Percent 669 Obesity_Rate_Percent 669 Underweight_Rate_Percent 669 Overweight_Rate_Percent 669 Safe_Water_Access_Percent 669 Continent_Code 669 dtype: int64
Po wykluczeniu wierszy z pustymi wartościami baza zostaje okrojona do około 1/3 swej wielkości
Sprawdzenie przypisanie kodu kontynentu do państw
In [28]:
continent_counts = data['Continent_Code'].value_counts()
continent_counts
Out[28]:
Continent_Code AF 480 EU 430 AS 400 NA 190 Unknown 140 OC 130 SA 110 Name: count, dtype: int64
Wyświetlenie których państw biblioteka nie rozpoznała
In [30]:
unknown_countries = data[data['Continent_Code'] == 'Unknown']['Country'].unique()
unknown_countries
Out[30]:
array(['Bahamas, The', 'Congo, Dem. Rep.', 'Congo, Rep.', "Cote d'Ivoire",
'Egypt, Arab Rep.', 'Gambia, The', 'Iran, Islamic Rep.',
'Korea, Rep.', 'Lao PDR', 'Micronesia, Fed. Sts.', 'Timor-Leste',
'Turkiye', 'Venezuela, RB', 'Yemen, Rep.'], dtype=object)
Stworzenie listy i ręczne przypisanie państw do kontynentów
In [34]:
manual_continent_mapping = {
'Bahamas, The': 'NA',
'Congo, Dem. Rep.': 'AF',
'Congo, Rep.': 'AF',
"Cote d'Ivoire": 'AF',
'Egypt, Arab Rep.': 'AF',
'Gambia, The': 'AF',
'Iran, Islamic Rep.': 'AS',
'Korea, Rep.': 'AS',
'Lao PDR': 'AS',
'Micronesia, Fed. Sts.': 'OC',
'Timor-Leste': 'OC',
'Turkiye': 'AS',
'Venezuela, RB': 'SA',
'Yemen, Rep.': 'AS'
}
data['Continent_Code'] = data['Country'].apply(
lambda x: manual_continent_mapping[x] if x in manual_continent_mapping else data.loc[data['Country'] == x, 'Continent_Code'].iloc[0]
)
data[data['Country'].isin(manual_continent_mapping.keys())][['Country', 'Continent_Code']]
Out[34]:
| Country | Continent_Code | |
|---|---|---|
| 110 | Bahamas, The | NA |
| 111 | Bahamas, The | NA |
| 112 | Bahamas, The | NA |
| 113 | Bahamas, The | NA |
| 114 | Bahamas, The | NA |
| ... | ... | ... |
| 1855 | Yemen, Rep. | AS |
| 1856 | Yemen, Rep. | AS |
| 1857 | Yemen, Rep. | AS |
| 1858 | Yemen, Rep. | AS |
| 1859 | Yemen, Rep. | AS |
140 rows × 2 columns
Ponowne sprawdzenie czy wszytkie państwa mają przypisany kod kontynentu
In [36]:
continent_counts = data['Continent_Code'].value_counts()
continent_counts
Out[36]:
Continent_Code AF 530 AS 450 EU 430 NA 200 OC 150 SA 120 Name: count, dtype: int64
Operacja na danych
Wyświetlenie przewidywanych życia ogólne, kobiet i mężczyzn w latach 2012-2021 dla każdego kontynentu
In [39]:
filtered_data = data[(data['Year'] >= 2012) & (data['Year'] <= 2021)]
grouped_data = filtered_data.groupby(['Continent_Code', 'Year'])[
['Life_Expectancy', 'Life_Expectancy_Female', 'Life_Expectancy_Male']
].mean().reset_index()
continents = [c for c in grouped_data['Continent_Code'].unique() if c != "Unknown"]
fig, axes = plt.subplots(3, 2, figsize=(15, 18))
axes = axes.flatten()
for i, continent in enumerate(continents[:6]):
continent_data = grouped_data[grouped_data['Continent_Code'] == continent]
axes[i].plot(continent_data['Year'], continent_data['Life_Expectancy'], label='Life Expectancy')
axes[i].plot(continent_data['Year'], continent_data['Life_Expectancy_Female'], label='Life Expectancy (Female)')
axes[i].plot(continent_data['Year'], continent_data['Life_Expectancy_Male'], label='Life Expectancy (Male)')
axes[i].set_title(f'Life Expectancy Trends in {continent} (2012-2021)')
axes[i].set_xlabel('Year')
axes[i].set_ylabel('Life Expectancy')
axes[i].legend()
axes[i].grid(True)
for j in range(len(continents), len(axes)):
fig.delaxes(axes[j])
plt.tight_layout()
plt.show()
Wyświetlenie liczby ludności Polski i jej sąsiadów w latach 2012-2021 również z podziałem na liczbę kobiet i mężczyzn
In [122]:
poland_and_neighbors = [
'Poland', 'Germany', 'Czechia', 'Slovak Republic',
'Ukraine', 'Belarus', 'Lithuania', 'Russian Federation'
]
# Filtrowanie danych dla Polski i jej sąsiadów oraz lat 2012-2021
filtered_data = data[
(data['Country'].isin(poland_and_neighbors)) &
(data['Year'] >= 2012) & (data['Year'] <= 2021)
]
fig, axes = plt.subplots(10, 1, figsize=(30, 150), constrained_layout=True)
axes = axes.flatten()
years = range(2012, 2022)
for i, year in enumerate(years):
ax = axes[i]
year_data = filtered_data[filtered_data['Year'] == year]
x = year_data['Country']
bar_width = 0.25
x_positions = range(len(x))
bars_total = ax.bar(
[p - bar_width for p in x_positions],
year_data['Total_Population'],
width=bar_width,
label='Total Population'
)
bars_female = ax.bar(
x_positions,
year_data['Female_Population'],
width=bar_width,
label='Female Population',
alpha=0.7
)
bars_male = ax.bar(
[p + bar_width for p in x_positions],
year_data['Male_Population'],
width=bar_width,
label='Male Population',
alpha=0.7
)
for bar in bars_total + bars_female + bars_male:
height = bar.get_height()
ax.text(
bar.get_x() + bar.get_width() / 2.0,
height/2,
f'{int(height):,}',
ha='center',
va='bottom',
fontsize=20,
rotation=90
)
ax.set_title(f'Populacja Polski i jej sąsiadów w {year}', fontsize=50)
ax.set_xlabel('Country', fontsize=40)
ax.set_ylabel('Population', fontsize=40)
ax.set_xticks(range(len(x)))
ax.set_xticklabels(x, rotation=45, ha='right', fontsize=30)
ax.legend(fontsize=20)
ax.tick_params(axis='y', labelsize=20)
ax.yaxis.get_offset_text().set_fontsize(30)
for i in range(len(years), len(axes)):
fig.delaxes(axes[i])
plt.show()
Pokazanie Top 10 w liczbie ludności na świecie na przestrzeni lat 2012-2021
In [134]:
# Filtrowanie danych dla lat 2012-2021
filtered_data = data[(data['Year'] >= 2012) & (data['Year'] <= 2021)]
# Grupowanie danych, aby znaleźć Top 10 dla każdego roku
top_10_each_year = (
filtered_data.groupby('Year', group_keys=False)
.apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
)
# Podział danych top_10_each_year na poszczególne lata
years = filtered_data['Year'].unique()
top_10_each_year_dict = {
year: top_10_each_year[top_10_each_year['Year'] == year] for year in years
}
top_10_each_year_dict
C:\Users\valen\AppData\Local\Temp\ipykernel_6320\2792410456.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
Out[134]:
{2012: Year Country Total_Population
350 2012 China 1.354190e+09
760 2012 India 1.274487e+09
1790 2012 United States 3.138777e+08
770 2012 Indonesia 2.502227e+08
1300 2012 Pakistan 2.022059e+08
230 2012 Brazil 1.999777e+08
1260 2012 Nigeria 1.700759e+08
130 2012 Bangladesh 1.520906e+08
1410 2012 Russian Federation 1.433784e+08
840 2012 Japan 1.276290e+08,
2013: Year Country Total_Population
351 2013 China 1.363240e+09
761 2013 India 1.291132e+09
1791 2013 United States 3.160599e+08
771 2013 Indonesia 2.532759e+08
1301 2013 Pakistan 2.053376e+08
231 2013 Brazil 2.017218e+08
1261 2013 Nigeria 1.747261e+08
131 2013 Bangladesh 1.540301e+08
1411 2013 Russian Federation 1.438056e+08
841 2013 Japan 1.274450e+08,
2014: Year Country Total_Population
352 2014 China 1.371860e+09
762 2014 India 1.307247e+09
1792 2014 United States 3.183863e+08
772 2014 Indonesia 2.562298e+08
1302 2014 Pakistan 2.082516e+08
232 2014 Brazil 2.034596e+08
1262 2014 Nigeria 1.793790e+08
132 2014 Bangladesh 1.559613e+08
1412 2014 Russian Federation 1.442372e+08
842 2014 Japan 1.272760e+08,
2015: Year Country Total_Population
353 2015 China 1.379860e+09
763 2015 India 1.322867e+09
1793 2015 United States 3.207390e+08
773 2015 Indonesia 2.590920e+08
1303 2015 Pakistan 2.109693e+08
233 2015 Brazil 2.051882e+08
1263 2015 Nigeria 1.839958e+08
133 2015 Bangladesh 1.578300e+08
1413 2015 Russian Federation 1.446407e+08
843 2015 Japan 1.271410e+08,
2016: Year Country Total_Population
354 2016 China 1.387790e+09
764 2016 India 1.338636e+09
1794 2016 United States 3.230718e+08
774 2016 Indonesia 2.618502e+08
1304 2016 Pakistan 2.135248e+08
234 2016 Brazil 2.068596e+08
1264 2016 Nigeria 1.886669e+08
134 2016 Bangladesh 1.597846e+08
1414 2016 Russian Federation 1.450155e+08
844 2016 Japan 1.270760e+08,
2017: Year Country Total_Population
355 2017 China 1.396215e+09
765 2017 India 1.354196e+09
1795 2017 United States 3.251221e+08
775 2017 Indonesia 2.644989e+08
1305 2017 Pakistan 2.163797e+08
235 2017 Brazil 2.085050e+08
1265 2017 Nigeria 1.934959e+08
135 2017 Bangladesh 1.617940e+08
1415 2017 Russian Federation 1.452933e+08
845 2017 Japan 1.269720e+08,
2018: Year Country Total_Population
356 2018 China 1.402760e+09
766 2018 India 1.369003e+09
1796 2018 United States 3.268382e+08
776 2018 Indonesia 2.670668e+08
1306 2018 Pakistan 2.197315e+08
236 2018 Brazil 2.101666e+08
1266 2018 Nigeria 1.983876e+08
136 2018 Bangladesh 1.636840e+08
1416 2018 Russian Federation 1.453981e+08
846 2018 Japan 1.268110e+08,
2019: Year Country Total_Population
357 2019 China 1.407745e+09
767 2019 India 1.383112e+09
1797 2019 United States 3.283300e+08
777 2019 Indonesia 2.695829e+08
1307 2019 Pakistan 2.232933e+08
237 2019 Brazil 2.117829e+08
1267 2019 Nigeria 2.033045e+08
137 2019 Bangladesh 1.655162e+08
1417 2019 Russian Federation 1.454533e+08
847 2019 Japan 1.266330e+08,
2020: Year Country Total_Population
358 2020 China 1.411100e+09
768 2020 India 1.396387e+09
1798 2020 United States 3.315269e+08
778 2020 Indonesia 2.718580e+08
1308 2020 Pakistan 2.271967e+08
238 2020 Brazil 2.131963e+08
1268 2020 Nigeria 2.083274e+08
138 2020 Bangladesh 1.674210e+08
1418 2020 Russian Federation 1.452451e+08
848 2020 Japan 1.262610e+08,
2021: Year Country Total_Population
359 2021 China 1.412360e+09
769 2021 India 1.407564e+09
1799 2021 United States 3.320490e+08
779 2021 Indonesia 2.737532e+08
1309 2021 Pakistan 2.314021e+08
239 2021 Brazil 2.143262e+08
1269 2021 Nigeria 2.134013e+08
139 2021 Bangladesh 1.693563e+08
1419 2021 Russian Federation 1.447468e+08
1109 2021 Mexico 1.267051e+08}
Top 10 Liczby ludności dla Afryki
In [144]:
africa_data = data[(data['Continent_Code'] == 'AF') & (data['Year'] >= 2012) & (data['Year'] <= 2021)]
top_10_africa_each_year = (
africa_data.groupby('Year', group_keys=False)
.apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
)
years_africa = filtered_data['Year'].unique()
top_10_africa_each_year_dict = {
year: top_10_africa_each_year[top_10_africa_each_year['Year'] == year] for year in years
}
top_10_africa_each_year_dict
C:\Users\valen\AppData\Local\Temp\ipykernel_6320\2975718093.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
Out[144]:
{2012: Year Country Total_Population
1260 2012 Nigeria 170075932.0
570 2012 Ethiopia 94451280.0
510 2012 Egypt, Arab Rep. 91240376.0
380 2012 Congo, Dem. Rep. 70997870.0
1550 2012 South Africa 53145033.0
1650 2012 Tanzania 47786137.0
870 2012 Kenya 43725806.0
20 2012 Algeria 37260563.0
1590 2012 Sudan 35159792.0
1750 2012 Uganda 34273295.0,
2013: Year Country Total_Population
1261 2013 Nigeria 174726123.0
571 2013 Ethiopia 97084366.0
511 2013 Egypt, Arab Rep. 93377890.0
381 2013 Congo, Dem. Rep. 73460021.0
1551 2013 South Africa 53873616.0
1651 2013 Tanzania 49253643.0
871 2013 Kenya 44792368.0
21 2013 Algeria 38000626.0
1591 2013 Sudan 35990704.0
1751 2013 Uganda 35273570.0,
2014: Year Country Total_Population
1262 2014 Nigeria 179379016.0
572 2014 Ethiopia 99746766.0
512 2014 Egypt, Arab Rep. 95592324.0
382 2014 Congo, Dem. Rep. 76035588.0
1552 2014 South Africa 54729551.0
1652 2014 Tanzania 50814552.0
872 2014 Kenya 45831863.0
22 2014 Algeria 38760168.0
1592 2014 Sudan 37003245.0
1752 2014 Uganda 36336539.0,
2015: Year Country Total_Population
1263 2015 Nigeria 183995785.0
573 2015 Ethiopia 102471895.0
513 2015 Egypt, Arab Rep. 97723799.0
383 2015 Congo, Dem. Rep. 78656904.0
1553 2015 South Africa 55876504.0
1653 2015 Tanzania 52542823.0
873 2015 Kenya 46851488.0
23 2015 Algeria 39543154.0
1593 2015 Sudan 38171178.0
1753 2015 Uganda 37477356.0,
2016: Year Country Total_Population
1264 2016 Nigeria 188666931.0
574 2016 Ethiopia 105293228.0
514 2016 Egypt, Arab Rep. 99784030.0
384 2016 Congo, Dem. Rep. 81430977.0
1554 2016 South Africa 56422274.0
1654 2016 Tanzania 54401802.0
874 2016 Kenya 47894670.0
24 2016 Algeria 40339329.0
1594 2016 Sudan 39377169.0
1754 2016 Uganda 38748299.0,
2017: Year Country Total_Population
1265 2017 Nigeria 193495907.0
575 2017 Ethiopia 108197950.0
515 2017 Egypt, Arab Rep. 101789386.0
385 2017 Congo, Dem. Rep. 84283273.0
1555 2017 South Africa 56641209.0
1655 2017 Tanzania 56267032.0
875 2017 Kenya 48948137.0
25 2017 Algeria 41136546.0
1595 2017 Sudan 40679828.0
1755 2017 Uganda 40127085.0,
2018: Year Country Total_Population
1266 2018 Nigeria 198387623.0
576 2018 Ethiopia 111129438.0
516 2018 Egypt, Arab Rep. 103740765.0
386 2018 Congo, Dem. Rep. 87087355.0
1656 2018 Tanzania 58090443.0
1556 2018 South Africa 57339635.0
876 2018 Kenya 49953304.0
1596 2018 Sudan 41999059.0
26 2018 Algeria 41927007.0
1756 2018 Uganda 41515395.0,
2019: Year Country Total_Population
1267 2019 Nigeria 203304492.0
577 2019 Ethiopia 114120594.0
517 2019 Egypt, Arab Rep. 105618671.0
387 2019 Congo, Dem. Rep. 89906890.0
1657 2019 Tanzania 59872579.0
1557 2019 South Africa 58087055.0
877 2019 Kenya 50951450.0
1597 2019 Sudan 43232093.0
1757 2019 Uganda 42949080.0
27 2019 Algeria 42705368.0,
2020: Year Country Total_Population
1268 2020 Nigeria 208327405.0
578 2020 Ethiopia 117190911.0
518 2020 Egypt, Arab Rep. 107465134.0
388 2020 Congo, Dem. Rep. 92853164.0
1658 2020 Tanzania 61704518.0
1558 2020 South Africa 58801927.0
878 2020 Kenya 51985780.0
1598 2020 Sudan 44440486.0
1758 2020 Uganda 44404611.0
28 2020 Algeria 43451666.0,
2021: Year Country Total_Population
1269 2021 Nigeria 213401323.0
579 2021 Ethiopia 120283026.0
519 2021 Egypt, Arab Rep. 109262178.0
389 2021 Congo, Dem. Rep. 95894118.0
1659 2021 Tanzania 63588334.0
1559 2021 South Africa 59392255.0
879 2021 Kenya 53005614.0
1759 2021 Uganda 45853778.0
1599 2021 Sudan 45657202.0
29 2021 Algeria 44177969.0}
Top 10 Liczby ludności dla Azji
In [148]:
asia_data = data[(data['Continent_Code'] == 'AS') & (data['Year'] >= 2012) & (data['Year'] <= 2021)]
top_10_asia_each_year = (
asia_data.groupby('Year', group_keys=False)
.apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
)
years_asia = filtered_data['Year'].unique()
top_10_asia_each_year_dict = {
year: top_10_asia_each_year[top_10_asia_each_year['Year'] == year] for year in years
}
top_10_asia_each_year_dict
C:\Users\valen\AppData\Local\Temp\ipykernel_6320\1338248896.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
Out[148]:
{2012: Year Country Total_Population
350 2012 China 1.354190e+09
760 2012 India 1.274487e+09
770 2012 Indonesia 2.502227e+08
1300 2012 Pakistan 2.022059e+08
130 2012 Bangladesh 1.520906e+08
840 2012 Japan 1.276290e+08
1360 2012 Philippines 9.803232e+07
1840 2012 Viet Nam 8.930133e+07
780 2012 Iran, Islamic Rep. 7.732445e+07
1720 2012 Turkiye 7.517583e+07,
2013: Year Country Total_Population
351 2013 China 1.363240e+09
761 2013 India 1.291132e+09
771 2013 Indonesia 2.532759e+08
1301 2013 Pakistan 2.053376e+08
131 2013 Bangladesh 1.540301e+08
841 2013 Japan 1.274450e+08
1361 2013 Philippines 9.970011e+07
1841 2013 Viet Nam 9.026774e+07
781 2013 Iran, Islamic Rep. 7.845893e+07
1721 2013 Turkiye 7.614762e+07,
2014: Year Country Total_Population
352 2014 China 1.371860e+09
762 2014 India 1.307247e+09
772 2014 Indonesia 2.562298e+08
1302 2014 Pakistan 2.082516e+08
132 2014 Bangladesh 1.559613e+08
842 2014 Japan 1.272760e+08
1362 2014 Philippines 1.013252e+08
1842 2014 Viet Nam 9.123550e+07
782 2014 Iran, Islamic Rep. 7.996167e+07
1722 2014 Turkiye 7.718188e+07,
2015: Year Country Total_Population
353 2015 China 1.379860e+09
763 2015 India 1.322867e+09
773 2015 Indonesia 2.590920e+08
1303 2015 Pakistan 2.109693e+08
133 2015 Bangladesh 1.578300e+08
843 2015 Japan 1.271410e+08
1363 2015 Philippines 1.030314e+08
1843 2015 Viet Nam 9.219140e+07
783 2015 Iran, Islamic Rep. 8.179084e+07
1723 2015 Turkiye 7.821848e+07,
2016: Year Country Total_Population
354 2016 China 1.387790e+09
764 2016 India 1.338636e+09
774 2016 Indonesia 2.618502e+08
1304 2016 Pakistan 2.135248e+08
134 2016 Bangladesh 1.597846e+08
844 2016 Japan 1.270760e+08
1364 2016 Philippines 1.048753e+08
1844 2016 Viet Nam 9.312653e+07
784 2016 Iran, Islamic Rep. 8.330623e+07
1724 2016 Turkiye 7.927796e+07,
2017: Year Country Total_Population
355 2017 China 1.396215e+09
765 2017 India 1.354196e+09
775 2017 Indonesia 2.644989e+08
1305 2017 Pakistan 2.163797e+08
135 2017 Bangladesh 1.617940e+08
845 2017 Japan 1.269720e+08
1365 2017 Philippines 1.067385e+08
1845 2017 Viet Nam 9.403305e+07
785 2017 Iran, Islamic Rep. 8.450508e+07
1725 2017 Turkiye 8.031270e+07,
2018: Year Country Total_Population
356 2018 China 1.402760e+09
766 2018 India 1.369003e+09
776 2018 Indonesia 2.670668e+08
1306 2018 Pakistan 2.197315e+08
136 2018 Bangladesh 1.636840e+08
846 2018 Japan 1.268110e+08
1366 2018 Philippines 1.085688e+08
1846 2018 Viet Nam 9.491433e+07
786 2018 Iran, Islamic Rep. 8.561756e+07
1726 2018 Turkiye 8.140720e+07,
2019: Year Country Total_Population
357 2019 China 1.407745e+09
767 2019 India 1.383112e+09
777 2019 Indonesia 2.695829e+08
1307 2019 Pakistan 2.232933e+08
137 2019 Bangladesh 1.655162e+08
847 2019 Japan 1.266330e+08
1367 2019 Philippines 1.103808e+08
1847 2019 Viet Nam 9.577672e+07
787 2019 Iran, Islamic Rep. 8.656420e+07
1727 2019 Turkiye 8.257944e+07,
2020: Year Country Total_Population
358 2020 China 1.411100e+09
768 2020 India 1.396387e+09
778 2020 Indonesia 2.718580e+08
1308 2020 Pakistan 2.271967e+08
138 2020 Bangladesh 1.674210e+08
848 2020 Japan 1.262610e+08
1368 2020 Philippines 1.121910e+08
1848 2020 Viet Nam 9.664868e+07
788 2020 Iran, Islamic Rep. 8.729019e+07
1728 2020 Turkiye 8.338468e+07,
2021: Year Country Total_Population
359 2021 China 1.412360e+09
769 2021 India 1.407564e+09
779 2021 Indonesia 2.737532e+08
1309 2021 Pakistan 2.314021e+08
139 2021 Bangladesh 1.693563e+08
849 2021 Japan 1.256816e+08
1369 2021 Philippines 1.138803e+08
1849 2021 Viet Nam 9.746803e+07
789 2021 Iran, Islamic Rep. 8.792343e+07
1729 2021 Turkiye 8.414732e+07}
Top 10 Liczby ludności dla Europy
In [150]:
europe_data = data[(data['Continent_Code'] == 'EU') & (data['Year'] >= 2012) & (data['Year'] <= 2021)]
top_10_europe_each_year = (
europe_data.groupby('Year', group_keys=False)
.apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
)
years_europe = filtered_data['Year'].unique()
top_10_europe_each_year_dict = {
year: top_10_europe_each_year[top_10_europe_each_year['Year'] == year] for year in years
}
top_10_europe_each_year_dict
C:\Users\valen\AppData\Local\Temp\ipykernel_6320\318427.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
Out[150]:
{2012: Year Country Total_Population
1410 2012 Russian Federation 143378447.0
640 2012 Germany 80425823.0
600 2012 France 65662240.0
1780 2012 United Kingdom 63700215.0
820 2012 Italy 59539717.0
1570 2012 Spain 46773055.0
1760 2012 Ukraine 45593342.0
1370 2012 Poland 38063164.0
1400 2012 Romania 20058035.0
1220 2012 Netherlands 16754962.0,
2013: Year Country Total_Population
1411 2013 Russian Federation 143805638.0
641 2013 Germany 80645605.0
601 2013 France 66002289.0
1781 2013 United Kingdom 64128273.0
821 2013 Italy 60233948.0
1571 2013 Spain 46620045.0
1761 2013 Ukraine 45489648.0
1371 2013 Poland 38040196.0
1401 2013 Romania 19983693.0
1221 2013 Netherlands 16804432.0,
2014: Year Country Total_Population
1412 2014 Russian Federation 144237223.0
642 2014 Germany 80982500.0
602 2014 France 66312067.0
1782 2014 United Kingdom 64602298.0
822 2014 Italy 60789140.0
1572 2014 Spain 46480882.0
1762 2014 Ukraine 45272155.0
1372 2014 Poland 38011735.0
1402 2014 Romania 19908979.0
1222 2014 Netherlands 16865008.0,
2015: Year Country Total_Population
1413 2015 Russian Federation 144640716.0
643 2015 Germany 81686611.0
603 2015 France 66548272.0
1783 2015 United Kingdom 65116219.0
823 2015 Italy 60730582.0
1573 2015 Spain 46444832.0
1763 2015 Ukraine 45167350.0
1373 2015 Poland 37986412.0
1403 2015 Romania 19815616.0
1223 2015 Netherlands 16939923.0,
2016: Year Country Total_Population
1414 2016 Russian Federation 145015460.0
644 2016 Germany 82348669.0
604 2016 France 66724104.0
1784 2016 United Kingdom 65611593.0
824 2016 Italy 60627498.0
1574 2016 Spain 46484062.0
1764 2016 Ukraine 45038236.0
1374 2016 Poland 37970087.0
1404 2016 Romania 19702267.0
1224 2016 Netherlands 17030314.0,
2017: Year Country Total_Population
1415 2017 Russian Federation 145293260.0
645 2017 Germany 82657002.0
605 2017 France 66918020.0
1785 2017 United Kingdom 66058859.0
825 2017 Italy 60536709.0
1575 2017 Spain 46593236.0
1765 2017 Ukraine 44880758.0
1375 2017 Poland 37974826.0
1405 2017 Romania 19588715.0
1225 2017 Netherlands 17131296.0,
2018: Year Country Total_Population
1416 2018 Russian Federation 145398106.0
646 2018 Germany 82905782.0
606 2018 France 67158348.0
1786 2018 United Kingdom 66460344.0
826 2018 Italy 60421760.0
1576 2018 Spain 46797754.0
1766 2018 Ukraine 44690584.0
1376 2018 Poland 37974750.0
1406 2018 Romania 19473970.0
1226 2018 Netherlands 17231624.0,
2019: Year Country Total_Population
1417 2019 Russian Federation 145453291.0
647 2019 Germany 83092962.0
607 2019 France 67388001.0
1787 2019 United Kingdom 66836327.0
827 2019 Italy 59729081.0
1577 2019 Spain 47134837.0
1767 2019 Ukraine 44474512.0
1377 2019 Poland 37965475.0
1407 2019 Romania 19371648.0
1227 2019 Netherlands 17344874.0,
2020: Year Country Total_Population
1418 2020 Russian Federation 145245148.0
648 2020 Germany 83160871.0
608 2020 France 67571107.0
1788 2020 United Kingdom 67081234.0
828 2020 Italy 59438851.0
1578 2020 Spain 47365655.0
1768 2020 Ukraine 44207754.0
1378 2020 Poland 37899070.0
1408 2020 Romania 19265250.0
1228 2020 Netherlands 17441500.0,
2021: Year Country Total_Population
1419 2021 Russian Federation 144746762.0
649 2021 Germany 83196078.0
609 2021 France 67764304.0
1789 2021 United Kingdom 67026292.0
829 2021 Italy 59133173.0
1579 2021 Spain 47415794.0
1769 2021 Ukraine 43848986.0
1379 2021 Poland 37747124.0
1409 2021 Romania 19122059.0
1229 2021 Netherlands 17533044.0}
Top 10 Liczby ludności dla Ameryki Północnej
In [160]:
NAmerica_data = data[(data['Continent_Code'] == 'NA') & (data['Year'] >= 2012) & (data['Year'] <= 2021)]
top_10_NAmerica_each_year = (
NAmerica_data.groupby('Year', group_keys=False)
.apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
)
years_NAmerica = filtered_data['Year'].unique()
top_10_NAmerica_each_year_dict = {
year: top_10_NAmerica_each_year[top_10_NAmerica_each_year['Year'] == year] for year in years
}
top_10_NAmerica_each_year_dict
C:\Users\valen\AppData\Local\Temp\ipykernel_6320\1161269664.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
Out[160]:
{2012: Year Country Total_Population
1790 2012 United States 313877662.0
1100 2012 Mexico 115755909.0
310 2012 Canada 34713395.0
680 2012 Guatemala 14781942.0
430 2012 Cuba 11309290.0
720 2012 Haiti 10108539.0
490 2012 Dominican Republic 10030882.0
730 2012 Honduras 8792367.0
520 2012 El Salvador 6161289.0
1240 2012 Nicaragua 6030607.0,
2013: Year Country Total_Population
1791 2013 United States 316059947.0
1101 2013 Mexico 117290686.0
311 2013 Canada 35080992.0
681 2013 Guatemala 15043981.0
431 2013 Cuba 11321579.0
721 2013 Haiti 10261206.0
491 2013 Dominican Republic 10157051.0
731 2013 Honduras 8960657.0
521 2013 El Salvador 6185642.0
1241 2013 Nicaragua 6119379.0,
2014: Year Country Total_Population
1792 2014 United States 318386329.0
1102 2014 Mexico 118755887.0
312 2014 Canada 35434066.0
682 2014 Guatemala 15306316.0
432 2014 Cuba 11332026.0
722 2014 Haiti 10412740.0
492 2014 Dominican Republic 10282115.0
732 2014 Honduras 9127846.0
522 2014 El Salvador 6209526.0
1242 2014 Nicaragua 6208676.0,
2015: Year Country Total_Population
1793 2015 United States 320738994.0
1103 2015 Mexico 120149897.0
313 2015 Canada 35704498.0
683 2015 Guatemala 15567419.0
433 2015 Cuba 11339894.0
723 2015 Haiti 10563757.0
493 2015 Dominican Republic 10405832.0
733 2015 Honduras 9294505.0
1243 2015 Nicaragua 6298598.0
523 2015 El Salvador 6231066.0,
2016: Year Country Total_Population
1794 2016 United States 323071755.0
1104 2016 Mexico 121519221.0
314 2016 Canada 36110803.0
684 2016 Guatemala 15827690.0
434 2016 Cuba 11342012.0
724 2016 Haiti 10713849.0
494 2016 Dominican Republic 10527592.0
734 2016 Honduras 9460798.0
1244 2016 Nicaragua 6389235.0
524 2016 El Salvador 6250510.0,
2017: Year Country Total_Population
1795 2017 United States 325122128.0
1105 2017 Mexico 122839258.0
315 2017 Canada 36545075.0
685 2017 Guatemala 16087418.0
435 2017 Cuba 11336405.0
725 2017 Haiti 10863543.0
495 2017 Dominican Republic 10647244.0
735 2017 Honduras 9626842.0
1245 2017 Nicaragua 6480532.0
525 2017 El Salvador 6266654.0,
2018: Year Country Total_Population
1796 2018 United States 326838199.0
1106 2018 Mexico 124013861.0
316 2018 Canada 37072620.0
686 2018 Guatemala 16346950.0
436 2018 Cuba 11328244.0
726 2018 Haiti 11012421.0
496 2018 Dominican Republic 10765531.0
736 2018 Honduras 9792850.0
1246 2018 Nicaragua 6572233.0
526 2018 El Salvador 6276342.0,
2019: Year Country Total_Population
1797 2019 United States 328329953.0
1107 2019 Mexico 125085311.0
317 2019 Canada 37618495.0
687 2019 Guatemala 16604026.0
437 2019 Cuba 11316697.0
727 2019 Haiti 11160438.0
497 2019 Dominican Republic 10881882.0
737 2019 Honduras 9958829.0
1247 2019 Nicaragua 6663924.0
527 2019 El Salvador 6280217.0,
2020: Year Country Total_Population
1798 2020 United States 331526933.0
1108 2020 Mexico 125998302.0
318 2020 Canada 38028638.0
688 2020 Guatemala 16858333.0
728 2020 Haiti 11306801.0
438 2020 Cuba 11300698.0
498 2020 Dominican Republic 10999664.0
738 2020 Honduras 10121763.0
1248 2020 Nicaragua 6755895.0
528 2020 El Salvador 6292731.0,
2021: Year Country Total_Population
1799 2021 United States 332048977.0
1109 2021 Mexico 126705138.0
319 2021 Canada 38239864.0
689 2021 Guatemala 17109746.0
729 2021 Haiti 11447569.0
439 2021 Cuba 11256372.0
499 2021 Dominican Republic 11117873.0
739 2021 Honduras 10278345.0
1249 2021 Nicaragua 6850540.0
529 2021 El Salvador 6314167.0}
Top 10 Liczby ludności dla Oceanii
In [162]:
oceania_data = data[(data['Continent_Code'] == 'OC') & (data['Year'] >= 2012) & (data['Year'] <= 2021)]
top_10_oceania_each_year = (
oceania_data.groupby('Year', group_keys=False)
.apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
)
years_oceania = filtered_data['Year'].unique()
top_10_oceania_each_year_dict = {
year: top_10_oceania_each_year[top_10_oceania_each_year['Year'] == year] for year in years
}
top_10_oceania_each_year_dict
C:\Users\valen\AppData\Local\Temp\ipykernel_6320\1565466495.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
Out[162]:
{2012: Year Country Total_Population
80 2012 Australia 22733465.0
1330 2012 Papua New Guinea 8026545.0
1230 2012 New Zealand 4408100.0
1670 2012 Timor-Leste 1137676.0
580 2012 Fiji 911059.0
1540 2012 Solomon Islands 567763.0
1820 2012 Vanuatu 257313.0
1430 2012 Samoa 198124.0
880 2012 Kiribati 111618.0
1110 2012 Micronesia, Fed. Sts. 108232.0,
2013: Year Country Total_Population
81 2013 Australia 23128129.0
1331 2013 Papua New Guinea 8245627.0
1231 2013 New Zealand 4442100.0
1671 2013 Timor-Leste 1161555.0
581 2013 Fiji 913453.0
1541 2013 Solomon Islands 582365.0
1821 2013 Vanuatu 263534.0
1431 2013 Samoa 199939.0
881 2013 Kiribati 113311.0
1111 2013 Micronesia, Fed. Sts. 108609.0,
2014: Year Country Total_Population
82 2014 Australia 23475686.0
1332 2014 Papua New Guinea 8464153.0
1232 2014 New Zealand 4516500.0
1672 2014 Timor-Leste 1184830.0
582 2014 Fiji 915560.0
1542 2014 Solomon Islands 597375.0
1822 2014 Vanuatu 269927.0
1432 2014 Samoa 201757.0
882 2014 Kiribati 114985.0
1112 2014 Micronesia, Fed. Sts. 109024.0,
2015: Year Country Total_Population
83 2015 Australia 23815995.0
1333 2015 Papua New Guinea 8682174.0
1233 2015 New Zealand 4609400.0
1673 2015 Timor-Leste 1205813.0
583 2015 Fiji 917200.0
1543 2015 Solomon Islands 612660.0
1823 2015 Vanuatu 276438.0
1433 2015 Samoa 203571.0
883 2015 Kiribati 116707.0
1113 2015 Micronesia, Fed. Sts. 109462.0,
2016: Year Country Total_Population
84 2016 Australia 24190907.0
1334 2016 Papua New Guinea 8899169.0
1234 2016 New Zealand 4714100.0
1674 2016 Timor-Leste 1224562.0
584 2016 Fiji 918371.0
1544 2016 Solomon Islands 628102.0
1824 2016 Vanuatu 283218.0
1434 2016 Samoa 205544.0
884 2016 Kiribati 118513.0
1114 2016 Micronesia, Fed. Sts. 109925.0,
2017: Year Country Total_Population
85 2017 Australia 24592588.0
1335 2017 Papua New Guinea 9114796.0
1235 2017 New Zealand 4813600.0
1675 2017 Timor-Leste 1243235.0
585 2017 Fiji 919019.0
1545 2017 Solomon Islands 643634.0
1825 2017 Vanuatu 290239.0
1435 2017 Samoa 207630.0
885 2017 Kiribati 120362.0
1115 2017 Micronesia, Fed. Sts. 110430.0,
2018: Year Country Total_Population
86 2018 Australia 24963258.0
1336 2018 Papua New Guinea 9329227.0
1236 2018 New Zealand 4900600.0
1676 2018 Timor-Leste 1261845.0
586 2018 Fiji 918996.0
1546 2018 Solomon Islands 659249.0
1826 2018 Vanuatu 297298.0
1436 2018 Samoa 209701.0
886 2018 Kiribati 122261.0
1116 2018 Micronesia, Fed. Sts. 110929.0,
2019: Year Country Total_Population
87 2019 Australia 25334826.0
1337 2019 Papua New Guinea 9542486.0
1237 2019 New Zealand 4979200.0
1677 2019 Timor-Leste 1280438.0
587 2019 Fiji 918465.0
1547 2019 Solomon Islands 674993.0
1827 2019 Vanuatu 304404.0
1437 2019 Samoa 211905.0
887 2019 Kiribati 124241.0
1117 2019 Micronesia, Fed. Sts. 111379.0,
2020: Year Country Total_Population
88 2020 Australia 25649248.0
1338 2020 Papua New Guinea 9749640.0
1238 2020 New Zealand 5090200.0
1678 2020 Timor-Leste 1299995.0
588 2020 Fiji 920422.0
1548 2020 Solomon Islands 691191.0
1828 2020 Vanuatu 311685.0
1438 2020 Samoa 214929.0
888 2020 Kiribati 126463.0
1118 2020 Micronesia, Fed. Sts. 112106.0,
2021: Year Country Total_Population
89 2021 Australia 25685412.0
1339 2021 Papua New Guinea 9949437.0
1239 2021 New Zealand 5111300.0
1679 2021 Timor-Leste 1320942.0
589 2021 Fiji 924610.0
1549 2021 Solomon Islands 707851.0
1829 2021 Vanuatu 319137.0
1439 2021 Samoa 218764.0
889 2021 Kiribati 128874.0
1119 2021 Micronesia, Fed. Sts. 113131.0}
Top 10 Liczby ludności dla Ameryki Południowej
In [166]:
SAmerica_data = data[(data['Continent_Code'] == 'SA') & (data['Year'] >= 2012) & (data['Year'] <= 2021)]
top_10_SAmerica_each_year = (
SAmerica_data.groupby('Year', group_keys=False)
.apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
)
years_SAmerica = filtered_data['Year'].unique()
top_10_SAmerica_each_year_dict = {
year: top_10_SAmerica_each_year[top_10_SAmerica_each_year['Year'] == year] for year in years
}
top_10_SAmerica_each_year_dict
C:\Users\valen\AppData\Local\Temp\ipykernel_6320\3321158501.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda x: x.nlargest(10, 'Total_Population')[['Year', 'Country', 'Total_Population']])
Out[166]:
{2012: Year Country Total_Population
230 2012 Brazil 199977707.0
360 2012 Colombia 45782417.0
60 2012 Argentina 41733271.0
1350 2012 Peru 29749589.0
1830 2012 Venezuela, RB 29470426.0
340 2012 Chile 17341771.0
500 2012 Ecuador 15483883.0
200 2012 Bolivia 10569697.0
1340 2012 Paraguay 5923322.0
1800 2012 Uruguay 3371133.0,
2013: Year Country Total_Population
231 2013 Brazil 201721767.0
361 2013 Colombia 46237930.0
61 2013 Argentina 42202935.0
1351 2013 Peru 30038809.0
1831 2013 Venezuela, RB 29838021.0
341 2013 Chile 17509925.0
501 2013 Ecuador 15722989.0
201 2013 Bolivia 10743349.0
1341 2013 Paraguay 6005652.0
1801 2013 Uruguay 3381180.0,
2014: Year Country Total_Population
232 2014 Brazil 203459650.0
362 2014 Colombia 46677947.0
62 2014 Argentina 42669500.0
1352 2014 Peru 30353951.0
1832 2014 Venezuela, RB 30193258.0
342 2014 Chile 17687108.0
502 2014 Ecuador 15957994.0
202 2014 Bolivia 10916987.0
1342 2014 Paraguay 6090721.0
1802 2014 Uruguay 3391662.0,
2015: Year Country Total_Population
233 2015 Brazil 205188205.0
363 2015 Colombia 47119728.0
63 2015 Argentina 43131966.0
1353 2015 Peru 30711863.0
1833 2015 Venezuela, RB 30529716.0
343 2015 Chile 17870124.0
503 2015 Ecuador 16195902.0
203 2015 Bolivia 11090085.0
1343 2015 Paraguay 6177950.0
1803 2015 Uruguay 3402818.0,
2016: Year Country Total_Population
234 2016 Brazil 206859578.0
364 2016 Colombia 47625955.0
64 2016 Argentina 43590368.0
1354 2016 Peru 31132779.0
1834 2016 Venezuela, RB 30741464.0
344 2016 Chile 18083879.0
504 2016 Ecuador 16439585.0
204 2016 Bolivia 11263015.0
1344 2016 Paraguay 6266615.0
1804 2016 Uruguay 3413766.0,
2017: Year Country Total_Population
235 2017 Brazil 208504960.0
365 2017 Colombia 48351671.0
65 2017 Argentina 44044811.0
1355 2017 Peru 31605486.0
1835 2017 Venezuela, RB 30563433.0
345 2017 Chile 18368577.0
505 2017 Ecuador 16696944.0
205 2017 Bolivia 11435533.0
1345 2017 Paraguay 6355404.0
1805 2017 Uruguay 3422200.0,
2018: Year Country Total_Population
236 2018 Brazil 210166592.0
366 2018 Colombia 49276961.0
66 2018 Argentina 44494502.0
1356 2018 Peru 32203944.0
1836 2018 Venezuela, RB 29825653.0
346 2018 Chile 18701450.0
506 2018 Ecuador 17015672.0
206 2018 Bolivia 11606905.0
1346 2018 Paraguay 6443328.0
1806 2018 Uruguay 3427042.0,
2019: Year Country Total_Population
237 2019 Brazil 211782878.0
367 2019 Colombia 50187406.0
67 2019 Argentina 44938712.0
1357 2019 Peru 32824861.0
1837 2019 Venezuela, RB 28971683.0
347 2019 Chile 19039485.0
507 2019 Ecuador 17343740.0
207 2019 Bolivia 11777315.0
1347 2019 Paraguay 6530026.0
1807 2019 Uruguay 3428409.0,
2020: Year Country Total_Population
238 2020 Brazil 213196304.0
368 2020 Colombia 50930662.0
68 2020 Argentina 45376763.0
1358 2020 Peru 33304756.0
1838 2020 Venezuela, RB 28490453.0
348 2020 Chile 19300315.0
508 2020 Ecuador 17588595.0
208 2020 Bolivia 11936162.0
1348 2020 Paraguay 6618695.0
1808 2020 Uruguay 3429086.0,
2021: Year Country Total_Population
239 2021 Brazil 214326223.0
369 2021 Colombia 51516562.0
69 2021 Argentina 45808747.0
1359 2021 Peru 33715471.0
1839 2021 Venezuela, RB 28199867.0
349 2021 Chile 19493184.0
509 2021 Ecuador 17797737.0
209 2021 Bolivia 12079472.0
1349 2021 Paraguay 6703799.0
1809 2021 Uruguay 3426260.0}
In [ ]:
Wydatki sanitarne na PKB %
In [193]:
continents = {
'AF': 'Africa',
'EU': 'Europe',
'AS': 'Asia',
'NA': 'North America',
'SA': 'South America',
'OC': 'Oceania'
}
fig, axes = plt.subplots(6, 1, figsize=(18, 72), constrained_layout=True)
axes = axes.flatten()
for idx, (continent_code, continent_name) in enumerate(continents.items()):
continent_data = data[data['Continent_Code'] == continent_code]
avg_sanitary_expense = (
continent_data.groupby('Country')['Sanitary_Expense_Per_GDP'].mean().reset_index()
)
top_10_countries = avg_sanitary_expense.sort_values(
by='Sanitary_Expense_Per_GDP', ascending=False
).head(10)
top_countries_data = data[
(data['Country'].isin(top_10_countries['Country'])) &
(data['Continent_Code'] == continent_code)
]
ax = axes[idx]
for country in top_10_countries['Country']:
country_data = top_countries_data[top_countries_data['Country'] == country]
ax.plot(
country_data['Year'],
country_data['Sanitary_Expense_Per_GDP'],
label=country
)
ax.set_title(f'Sanitary Expense (% GDP) in {continent_name}', fontsize=42)
ax.set_xlabel('Year', fontsize=36)
ax.set_ylabel('Sanitary Expense (% GDP)', fontsize=36)
ax.tick_params(axis='x', labelsize=14)
ax.tick_params(axis='y', labelsize=14)
ax.legend(fontsize=16, loc='upper left')
ax.grid(True)
for idx in range(len(continents), len(axes)):
fig.delaxes(axes[idx])
plt.show()
Ile przypada łóżek szpitalnych na 1000 osób w top 10 państwach na kontynentach
In [332]:
fig, axes = plt.subplots(6, 1, figsize=(12, 30), constrained_layout=True)
axes = axes.flatten()
for idx, (continent_code, continent_name) in enumerate(continents.items()):
continent_data = data[data['Continent_Code'] == continent_code]
countries_with_full_data = continent_data.groupby('Country').filter(
lambda x: x['Hospital_Beds_Per_1000'].notna().sum() == 10
)['Country'].unique()
avg_hospital_beds = (
continent_data[continent_data['Country'].isin(countries_with_full_data)]
.groupby('Country')['Hospital_Beds_Per_1000']
.mean()
.reset_index()
)
top_10_countries = avg_hospital_beds.sort_values(
by='Hospital_Beds_Per_1000', ascending=False
).head(10)
top_countries_data = data[
(data['Country'].isin(top_10_countries['Country'])) &
(data['Continent_Code'] == continent_code)
]
ax = axes[idx]
for country in top_10_countries['Country']:
country_data = top_countries_data[top_countries_data['Country'] == country]
ax.plot(
country_data['Year'],
country_data['Hospital_Beds_Per_1000'],
label=country
)
ax.set_title(f'Hospital Beds per 1000 in {continent_name}', fontsize=14)
ax.set_xlabel('Year', fontsize=12)
ax.set_ylabel('Beds per 1000', fontsize=12)
ax.tick_params(axis='x', labelsize=12)
ax.tick_params(axis='y', labelsize=12)
ax.legend(fontsize=8, loc='upper left')
ax.grid(True)
plt.show()
Zwizualizowanie top 10 państw w poziomie zaszczepienia z podziałem na kontynenty
In [237]:
fig, axes = plt.subplots(6, 1, figsize=(12, 30), constrained_layout=True)
axes = axes.flatten()
for idx, (continent_code, continent_name) in enumerate(continents.items()):
continent_data = data[data['Continent_Code'] == continent_code]
avg_immunization_rate = (
continent_data.groupby('Country')['Immunization_Rate']
.mean()
.reset_index()
)
top_10_countries = avg_immunization_rate.sort_values(
by='Immunization_Rate', ascending=False
).head(10)
top_countries_data = data[
(data['Country'].isin(top_10_countries['Country'])) &
(data['Continent_Code'] == continent_code)
]
ax = axes[idx]
years = sorted(top_countries_data['Year'].unique())
countries = top_10_countries['Country']
bar_width = 0.07
x_positions = range(len(years))
for i, country in enumerate(countries):
country_data = top_countries_data[top_countries_data['Country'] == country]
ax.bar(
[x + (i * bar_width) for x in x_positions],
country_data['Immunization_Rate'],
width=bar_width,
label=country
)
ax.set_title(f'Immunization Rate in {continent_name}', fontsize=14)
ax.set_xlabel('Year', fontsize=12)
ax.set_ylabel('Immunization Rate', fontsize=12)
ax.set_xticks([x + (len(countries) * bar_width) / 2 for x in x_positions])
ax.set_xticklabels(years)
ax.tick_params(axis='x', labelsize=10, rotation=45)
ax.tick_params(axis='y', labelsize=12)
ax.legend(fontsize=8, loc='lower left')
ax.grid(True)
plt.show()
Zwizualizowanie 5 losowych państw w poziomie zaszczepienia z podziałem na kontynenty
In [238]:
fig, axes = plt.subplots(6, 1, figsize=(14, 40), constrained_layout=True)
axes = axes.flatten()
for idx, (continent_code, continent_name) in enumerate(continents.items()):
continent_data = data[data['Continent_Code'] == continent_code]
selected_countries = continent_data['Country'].drop_duplicates().sample(5, random_state=42)
selected_data = continent_data[continent_data['Country'].isin(selected_countries)]
ax = axes[idx]
years = sorted(selected_data['Year'].unique())
bar_width = 0.15
x_positions = range(len(years))
for i, country in enumerate(selected_countries):
country_data = selected_data[selected_data['Country'] == country]
ax.bar(
[x + (i * bar_width) for x in x_positions],
country_data['Immunization_Rate'],
width=bar_width,
label=country
)
ax.set_title(f'Immunization Rate in {continent_name}', fontsize=16)
ax.set_xlabel('Year', fontsize=14)
ax.set_ylabel('Immunization Rate', fontsize=14)
ax.set_xticks([x + (len(selected_countries) * bar_width) / 2 for x in x_positions])
ax.set_xticklabels(years, fontsize=12, rotation=45)
ax.tick_params(axis='y', labelsize=12)
ax.legend(fontsize=12, loc='lower left')
ax.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()
Pokazanie procenta bezrobocia w Europie, porówannie roku 2012 z 2021 z wyróżnieniem Polski na wykresach
In [265]:
europe_data = data[(data['Continent_Code'] == 'EU') & (data['Year'].isin([2012, 2021]))]
fig, axes = plt.subplots(2, 1, figsize=(12, 14), constrained_layout=True)
for i, year in enumerate([2012, 2021]):
year_data = europe_data[europe_data['Year'] == year]
sorted_data = year_data[['Country', 'Unemployment_Rate']].dropna().sort_values(by='Unemployment_Rate', ascending=False)
colors = []
for _, row in sorted_data.iterrows():
if row['Country'] == 'Poland':
if row['Unemployment_Rate'] > 10:
colors.append('darkred')
elif 5 <= row['Unemployment_Rate'] <= 10:
colors.append('darkgoldenrod')
else:
colors.append('darkgreen')
else:
if row['Unemployment_Rate'] > 10:
colors.append('red')
elif 5 <= row['Unemployment_Rate'] <= 10:
colors.append('yellow')
else:
colors.append('green')
ax = axes[i]
bars = ax.barh(sorted_data['Country'], sorted_data['Unemployment_Rate'], color=colors)
ax.set_title(f'Unemployment Rate in Europe ({year})', fontsize=16)
ax.set_xlabel('Unemployment Rate (%)', fontsize=14)
ax.set_ylabel('Country', fontsize=14)
ax.invert_yaxis()
ax.grid(axis='x', linestyle='--', alpha=0.7)
for bar in bars:
ax.text(
bar.get_width() + 0.5,
bar.get_y() + bar.get_height() / 2,
f'{bar.get_width():.1f}%',
va='center',
fontsize=10
)
plt.show()
Pokazanie Procentu otyłości, niedowagi i nadwagi w Ameryce Północnej w 2021 roku
In [291]:
north_america_data = data[(data['Continent_Code'] == 'NA')]
variables = ['Obesity_Rate_Percent', 'Underweight_Rate_Percent', 'Overweight_Rate_Percent']
north_america_filtered = north_america_data.dropna(subset=variables)
latest_year = north_america_filtered['Year'].max()
latest_data = north_america_filtered[north_america_filtered['Year'] == latest_year]
fig, axes = plt.subplots(len(variables), 1, figsize=(12, 18), constrained_layout=True)
axes = axes.flatten()
for idx, variable in enumerate(variables):
ax = axes[idx]
sorted_data = latest_data[['Country', variable]].sort_values(by=variable, ascending=False)
bars = ax.barh(sorted_data['Country'], sorted_data[variable], color='blue')
for bar in bars:
ax.text(
bar.get_width() + 0.5,
bar.get_y() + bar.get_height() / 2,
f'{bar.get_width():.1f}',
va='center',
fontsize=10
)
ax.set_title(f'{variable.replace("_", " ")} in North America ({latest_year})', fontsize=14)
ax.set_xlabel(variable.replace("_", " "), fontsize=12)
ax.set_ylabel('Country', fontsize=12)
ax.invert_yaxis()
ax.grid(axis='x', linestyle='--', alpha=0.7)
plt.show()
Pokazanie średniej samobójstw przypadjącą na kontynet w 2012
In [380]:
data_2012 = data[(data['Year'] == 2012) & (data['Suicide_Rate_Percent'].notna())]
continent_suicide_rate = data_2012.groupby('Continent_Code')['Suicide_Rate_Percent'].mean().reset_index()
labels = continent_suicide_rate['Continent_Code']
sizes = continent_suicide_rate['Suicide_Rate_Percent']
plt.figure(figsize=(10, 6))
bars = plt.bar(labels, sizes, color=['#ff9999' if label == 'EU' else '#66b3ff' for label in labels])
plt.title('Average Suicide Rate Percent by Continent (2012)', fontsize=16)
plt.xlabel('Continent', fontsize=14)
plt.ylabel('Average Suicide Rate Percent', fontsize=14)
for bar in bars:
plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.1,
f'{bar.get_height():.1f}', ha='center', fontsize=12)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()
Pokazanie globalnego % wszystkich samobójstw na kontynet w 2012
In [399]:
data_2012 = data[(data['Year'] == 2012) & (data['Suicide_Rate_Percent'].notna())]
continent_suicide_rate = data_2012.groupby('Continent_Code')['Suicide_Rate_Percent'].mean().reset_index()
labels = [
f"{label} ({value:.1f}%)"
for label, value in zip(continent_suicide_rate['Continent_Code'], continent_suicide_rate['Suicide_Rate_Percent'])
]
sizes = continent_suicide_rate['Suicide_Rate_Percent']
explode = [0.2 if label.startswith('EU') else 0 for label in labels]
plt.figure(figsize=(10, 8))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', explode=explode, startangle=140, colors=['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'])
plt.title('Percentage of Global Suicides by Continent (2012)', fontsize=16)
plt.show()
Pokazanie top 10 państw z największym % samobójstwa
In [378]:
europe_data_2012 = data[
(data['Continent_Code'] == 'EU') &
(data['Year'] == 2012) &
(data['Suicide_Rate_Percent'].notna())
]
top_10_suicide_rate = europe_data_2012.nlargest(10, 'Suicide_Rate_Percent')[['Country', 'Suicide_Rate_Percent']]
top_10_suicide_rate
Out[378]:
| Country | Suicide_Rate_Percent | |
|---|---|---|
| 930 | Latvia | 23.53 |
| 1760 | Ukraine | 23.11 |
| 1530 | Slovenia | 21.95 |
| 550 | Estonia | 21.09 |
| 1370 | Poland | 18.51 |
| 420 | Croatia | 18.39 |
| 1480 | Serbia | 18.22 |
| 600 | France | 17.78 |
| 590 | Finland | 16.95 |
| 450 | Czechia | 16.70 |
Pokazanie średniej samobójstw przypadjącą na kontynet w 2019
In [382]:
data_2019 = data[(data['Year'] == 2019) & (data['Suicide_Rate_Percent'].notna())]
continent_suicide_rate = data_2019.groupby('Continent_Code')['Suicide_Rate_Percent'].mean().reset_index()
labels = continent_suicide_rate['Continent_Code']
sizes = continent_suicide_rate['Suicide_Rate_Percent']
plt.figure(figsize=(10, 6))
bars = plt.bar(labels, sizes, color=['#ff9999' if label == 'EU' else '#66b3ff' for label in labels])
plt.title('Average Suicide Rate Percent by Continent (2019)', fontsize=16)
plt.xlabel('Continent', fontsize=14)
plt.ylabel('Average Suicide Rate Percent', fontsize=14)
for bar in bars:
plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.1,
f'{bar.get_height():.1f}', ha='center', fontsize=12)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()
Pokazanie globalnego % wszystkich samobójstw na kontynet w 2019
In [410]:
data_2019 = data[(data['Year'] == 2019) & (data['Suicide_Rate_Percent'].notna())]
continent_suicide_rate = data_2019.groupby('Continent_Code')['Suicide_Rate_Percent'].mean().reset_index()
labels = [
f"{label} ({value:.1f}%)"
for label, value in zip(continent_suicide_rate['Continent_Code'], continent_suicide_rate['Suicide_Rate_Percent'])
]
sizes = continent_suicide_rate['Suicide_Rate_Percent']
explode = [0.2 if label.startswith('EU') else 0 for label in labels]
plt.figure(figsize=(10, 8))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', explode=explode, startangle=140, colors=['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'])
plt.title('Percentage of Global Suicides by Continent (2019)', fontsize=16)
plt.show()
Pokazanie top 10 państw z największym % samobójstwa
In [374]:
europe_data_2019 = data[
(data['Continent_Code'] == 'EU') &
(data['Year'] == 2019) &
(data['Suicide_Rate_Percent'].notna())
]
top_10_suicide_rate = europe_data_2019.nlargest(10, 'Suicide_Rate_Percent')[['Country', 'Suicide_Rate_Percent']]
top_10_suicide_rate
Out[374]:
| Country | Suicide_Rate_Percent | |
|---|---|---|
| 997 | Lithuania | 26.14 |
| 1417 | Russian Federation | 25.11 |
| 1767 | Ukraine | 21.63 |
| 157 | Belarus | 21.20 |
| 937 | Latvia | 20.12 |
| 1537 | Slovenia | 19.78 |
| 747 | Hungary | 16.65 |
| 1407 | Romania | 16.52 |
| 427 | Croatia | 16.37 |
| 807 | Ireland | 15.44 |
In [ ]: